Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, University of Maryland Medical Intelligent Imaging (UM2ii) Center, Baltimore, MD.
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
Curr Probl Diagn Radiol. 2022 Jul-Aug;51(4):552-555. doi: 10.1067/j.cpradiol.2022.01.004. Epub 2022 Jan 11.
Rapid advances in artificial intelligence (AI) have generated significant interest in the radiology community. However, formal AI initiatives and leadership roles in academic radiology has not been formally evaluated. The purpose of this study was to assess the prevalence of formal AI initiatives and leadership roles in academic radiology departments.
Radiology departments with National Institutes of Health funding in fiscal year 2019 were identified. AI educational and research initiatives, leadership roles, and industry partnerships were assessed by searching department websites for AI-related keywords. Correlations between NIH funding and the presence of AI initiatives were evaluated with linear regression.
Sixty-two radiology departments with NIH funding were included in this study. Educational initiatives on AI were offered by 29 (47%) departments. Fifty-five (89%) departments had at least 1 AI researcher and 34 (55%) departments were affiliated with an AI research laboratory, center, or cluster. AI-specific leadership roles and industry partnerships were identified in 3 (5%) and 23 (37%) departments, respectively. The amount of NIH funding did not have a significant linear correlation with educational initiatives (P = 0.08) but there was a significant linear correlation between funding and presence of research initiatives (P = 0.003).
AI educational initiatives were offered by almost half of radiology departments. Most departments had AI researchers and affiliated labs, but the majority were not led by the radiology department, and few had formal AI leadership roles. In the new AI era, these findings provide a benchmark for departments considering implementing formal AI initiatives.
人工智能(AI)的快速发展引起了放射学界的极大兴趣。然而,学术放射学中尚未正式评估正式的 AI 计划和领导角色。本研究的目的是评估学术放射科部门中正式的 AI 计划和领导角色的流行程度。
确定了在 2019 财年获得美国国立卫生研究院(NIH)资助的放射科。通过在部门网站上搜索与 AI 相关的关键字,评估 AI 教育和研究计划,领导角色和行业合作伙伴关系。使用线性回归评估 NIH 资助与 AI 计划存在之间的相关性。
本研究共纳入 62 个获得 NIH 资助的放射科。29 个(47%)部门提供了有关 AI 的教育计划。55 个(89%)部门至少有 1 个 AI 研究人员,34 个(55%)部门隶属于 AI 研究实验室,中心或集群。在 3 个(5%)和 23 个(37%)部门中分别确定了 AI 特定的领导角色和行业合作伙伴关系。 NIH 资助的金额与教育计划之间没有显着的线性相关性(P=0.08),但是与研究计划的存在之间存在显着的线性相关性(P=0.003)。
近一半的放射科提供了 AI 教育计划。大多数部门都有 AI 研究人员和附属实验室,但大多数都不是由放射科领导的,并且很少有正式的 AI 领导角色。在新的 AI 时代,这些发现为正在考虑实施正式 AI 计划的部门提供了基准。